Does Internet development affect urban-rural income gap in China? An empirical investigation at provincial level

Abstract

This study aims to examine the impact of Internet development on the urban-rural income gap in China. By using a provincial level panel dataset comprising 31 of China’s provinces, it analyzes and compares the effects of the eastern, central, and western regions over the period of 2005–2016. The results show that Internet development aggravates the gap in the central region much more than that in the eastern and western regions. The trade openness expands the urban-rural income gap only in the eastern region. Urbanization reduces the urban-rural income gap in the western region more than that in the eastern and central regions. Additionally, the regional economic development level also reduces the urban-rural income gap in central region more than that in the eastern region. FDI reduces the urban-rural income gap only in the central region. Additionally, while the urban-rural income gap can widen further by Internet development with trade openness, it can be decreased if Internet development is combined with FDI and urbanization. To reduce urban-rural income gap, the government should accelerate the construction of Internet according to regional differences.

Keywords

Internet development regional differences trade openness income gap urbanization

Introduction

In the 21st century, Internet technology has witnessed explosive development and popularization in China. As a revolutionary tool, Internet is profoundly changing the productivity and life of human beings. According to the latest statistics, China had 802 million Internet users in the first half of 2018, 3.8 percent more than that at the end of 2017, and the Internet penetration rate reached 57.7 percent. Among the current users, the number of rural Internet users totaled 211 million, accounting for 37.41% of total rural population. The number of urban Internet users totaled 591 million, accounting for 71.09% of the total urban population (China Internet Network Information Center, 2018). In the process of China’s continuous urbanization, the shifting of rural populations to urban areas will also lead to subtle changes in the structure of groups of urban and rural Internet users. It can be seen that there are still large differences in Internet penetration among urban and rural residents in China, and the resulting differences in Internet usage ability and frequency among urban and rural residents may affect the income gap and widen the digital divide. At the same time, such differences may also exist between the eastern, central and western provinces.

From the perspective of the urban-rural income gap, according to China’s national bureau of statistics, the income gap between urban and rural residents was still wide in the first half of 2018. Among the provinces with the largest gap, the per capita disposable income of urban residents was nearly 10 times higher that of the rural residents. Therefore, the Chinese government has implemented a series of solutions to promote the Internet¹, address the socioeconomic aspects elaborately, and foster sustainable socioeconomic development in the region, such as the ‘broadband China’ strategy, guidelines on actively advancing the ‘Internet plus’ initiative, and taking ‘broadband China’ and ‘Internet plus’ plan as the national strategy. Here, the ‘Internet plus’ refers to the information superhighway and industry 4.0 in China, and it was proposed by China’s Premier Li keqiang in the government work report on March 5, 2015.

Several academic studies have taken a variety of perspectives on Internet development while analyzing how it influences income distribution (Mack and Faggian, 2013; Najarzadeh et al., 2014; Whitacre et al., 2014; Celbis and Crombrugghe, 2018; Hübler and Hartje, 2016; Whitacre et al., 2018; Gao et al., 2018; Ma et al., 2018; Tchamyou et al., 2019). However, few studies have analyzed the effects of Internet development on the urban-rural income gap in China (Gao et al., 2018, Li et al., 2020, Qiu et al., 2021).

The study aims to analyze whether and how Internet development has affected the urban-rural income gap in China, by comparing the eastern, central, and western regions. Additionally, this study introduces interaction terms of Internet development with foreign direct investment (FDI), urbanization, and trade openness, and explores whether they contribute to narrowing the urban-rural income gap. Furthermore, this study also considers other economic factors that affect the urban-rural income gap, such as trade openness, urbanization, gross regional domestic product (GRDP), and FDI.

The remainder of the paper is organized as follows: the second section reviews the literature, which talks about Internet development, income distribution, and economic factors. The third section explains the current study’s empirical methodology including models, the analysis of the data, and analytical results. The fourth section presents the conclusions of this study and policy implications.

Literature review

Internet development and income distribution

In term of the relationship between Internet and income distribution, the previous studies either analyzed the influence of the Internet on labor productivity, employment and regional economics (Mack and Faggian, 2013; Najarzadeh et al., 2014; Celbis and Crombrugghe, 2018; Whitacre et al., 2018; Tchamyou et al., 2019) or investigated the effects of Internet diffusion on rural developing countries at the level of individuals and households (Whitacre, 2010; Whitacre et al., 2014; Hübler and Hartje, 2016; Gao et al., 2018; Ma et al., 2018). By using a dynamic panel data approach and data from 108 countries from 1995 to 2010, Najarzadeh et al. (2014) reported that a 1% increase in Internet users adds $8.16 to the GDP per person. Concerning the regional economics, Tchamyou et al. (2019) investigated the effects of information and communication technology (ICT) on income inequality in 48 African countries based on the Generalized Method of Moments from 1996 to 2014. It was found that ICT reduced inequality through the formal financial sector. Meanwhile, by using absolute convergence models to research data for Turkey, Celbis and Crombrugghe (2018) found that Internet infrastructure could positively affect the per capita income and reduce the regional per capita income disparities.

At the level of individuals and households, Whitacre et al. (2014) conducted spatial and first differenced regressions and provided evidence of a positive correlation between the level of rural broadband adoption and an increase in the household income. Since computers and mobile phones are terminal devices used to establish a connection with the Internet, some studies examined the effect of computer penetration or the use of smartphone on rural residents’ income. Gao et al. (2018) used a dynamic panel threshold effects model to study provincial data in China from 2002 to 2013, revealing that the penetration of computer can positively affect farmers’ income, which is related to narrowing the urban-rural income gap. Ma et al. (2018) reported that the growth of rural individuals’ income through off-farm work can increase the use of smartphone in rural regions, which, in turn, can increase the income. By using an endogenous switching probit model and samples from 493 rural individuals in China, the study found that the average annual income of households engaged in non-agricultural activities and work related to use of smartphones was 3,430 yuan and 2,643 yuan higher than that of households engaged in full-time farming and those not using smartphones, respectively. Whitacre (2010) used a modified decomposition of the standard Oaxaca-Blinder technique to study the spread of Internet technology to rural areas from 2003 to 2006 and found that although the impact of Internet infrastructure on the income gap is less, the importance is increasing. Moreover, Hübler and Hartje (2016), by using a cross-sectional probit model and cross-sectional model and researching samples from the rural Southeast Asian countries, gave evidence that the impact of smartphones is positively related to individuals’ income in rural regions in developing countries.

Additionally, some studies examined the relationship between Internet and income distribution in cross-country studies. For instance, Zhang (2013) constructed an Internet consumption model to analyze the relationship between digital divide and income disparity among the five economic development country-level groups categorized by The World Bank. However, the result only showed a correlation and not a causal relationship. In contrast, the impact of income distribution on Internet use was also examined in cross-country studies. For example, Močnik and Širec (2010) used factor analysis to report a positive influence of income distribution on Internet use, but the level of this effect differed according to countries’ different income groups. Similarly, studies have shown the negative effects of the digital divide on Internet adoption. Noh and Yoo (2008) constructed a framework of a pure exchange overlapping generations model to discover the impact of Internet adoption and income distribution on economic growth from 1995 to 2002. In high-income countries, the Internet’s implicit effect on the economy is negative and the digital divide has curbed the economic effects of the Internet. Most recently, in terms of the dynamic association, Bauer (2018) explained that the impact level of the digital economy on income inequality is ambiguous and mainly dependent on the specific context and policy selection by using a simplified dynamic model of the interactions between ICTs and income distribution. Considering related specific factors, the relationship could be positive or negative.

Furthermore, the relationship between the rural Internet penetration rate and income distribution has practical significance for promoting rural regional economic development and urban-rural integration. Rural areas have been recognized as being downstream of the supply chain and at a disadvantage in income distribution (Li et al., 2020). With the popularization of the Internet and universality of information technology, it may help farmers to increase their income and reduce the urban-rural income gap (Whitacre and Mills, 2010). Some studies have also confirmed that the digital dividend of the Internet is expanding in rural areas. Siaw et al. (2020) studied survey data from farmers in two regions of Ghana and found that their use of the Internet generated an income increase of 20.1% and 15.4% for farms and families, respectively. Data from Mexico also support this view, with Internet access helping reduce poverty levels in rural regions (Mora-Rivera and García-Mora, 2021). In addition, based on household-level survey data in rural China, Ma et al. (2018) found that the popularization of the Internet and smartphones has helped increase farm income and non-farm income in China by more than 10% and household income by about 14%. Therefore, we can infer that Internet penetration can help rural residents overcome barriers to market entry, reduce logistics costs, and generate income effects (Li et al., 2020). Although rural Internet penetration is rising, the number of users and network speed are nowhere near those in urban areas. The urban-rural income gap is caused by the differences, not only in the Internet penetration rate, but also in network speed, self-efficacy, and learning ability (Whitacre and Mills, 2007; LaRose et al., 2007; Ashmore et al., 2015).

Economic factors and income distribution

First, the study discusses the effect of economic indicators (gross domestic product (GDP), FDI, trade openness, and urbanization) on income distribution. GDP is a useful measure of output and applies to the estimates of productivity as an indicator of economic activity; thus, it was used as a predictor to examine income distribution in previous studies. The effect of economic development on the income gap has been verified in some large cities. Huang et al. (2016) used a series of sustainability indicators to assess the three dimensions of sustainability from 1978 to 2012. It was found that the Gini coefficient and the urban-rural income ratio were expanded, driven by unprecedented economic development. In recent years, the expansion of the income gap in most large cities could be suppressed. The relationship between economic factors and income inequality was also verified in regional studies. Tian et al. (2016) used the logit test to analyze regional income inequality and convergence in China from the perspective of club integration during 1978 to 2013. The results showed that income inequality before the club is related to economic growth, material and human capital, and income inequality within the club is decreasing. But at the same time, income inequality between clubs has deteriorated with economic development. Chang et al. (2018) investigated the interaction between GDP per capita and income inequality by adopting wavelet coherency analysis; they found a positive association between these two variables and provided evidence of a causality relationship from GDP per capita to income inequality.

There are significant cross-country differences in the long-term effects of FDI on income inequality. For some countries, the long-term effects of income inequality remained negative (Chintrakarn et al., 2012; Herzer and Nunnenkamp, 2013; Ucal et al., 2016; Paramati and Nguyen, 2018). Herzer and Nunnenkamp (2013) used panel cointegration techniques as well as unbalanced panel regressions and European data to examine the interaction between these two variables in the short- and long-terms. The results show that the growth of both inward and outward FDI can reduce income gap in the long-run, while the effect was reversed in the short-term. Additionally, it is worth mentioning that they found the causality relationship to be bidirectional in the long-term, which means that a decline in inequality can also contribute to a growth in FDI. Ucal et al. (2016) used the nonlinear auto-regressive distributed lag (ARDL) modelling approach to examine annual Turkish data from 1970 to 2008 and documented that FDI can lower the level of income inequality in both short- and long-terms, but the effect is limited. However, some countries witnessed a positive influence of FDI on income inequality (Lai, 2011; Herzer et al., 2014; Lin et al., 2015; Asteriou et al., 2014). In terms of China, Lai (2011) applied ordinary least-squares (OLS) to examine the micro-level effect of foreign investment firms on domestic wage inequality. They reported that foreign investment contributed to negative spillover effects on the wage level of domestic enterprises, thereby increasing the income inequality between firms significantly. By applying panel cointegration techniques as well as regression and data from Latin American countries, Herzer et al. (2014) also provided evidence of a positive influence of inward FDI on income inequality. In addition, FDI can also find great contributions to income inequality in developed countries. Asteriou et al. (2014) used panel regression models to study income inequality and globalization in EU-27 countries from 1995 to 2009 and found that the greatest contribution to inequality comes from FDI.

Regarding the linkage between trade openness and income inequality, Anderson (2005) highlighted that the growth of trade openness can influence income inequality in developing countries. For instance, greater trade openness can influence the spatial concentration of economic activity, eventually increasing the gap in the real income of fixed factors of production between regions. Many studies reported the effects of trade liberalization and financial globalization on income inequality. Jaumotte and Papageorgiou (2013) used the summary model, full model, and benchmark model to discuss the impact of the rapid development of trade and finance in most countries on income inequality from 1981 to 2003. They found that trade globalization, especially foreign direct investment, is associated with increased inequality. Bergh and Nilsson (2010) by least squares and country fixed effects to check whether the economic freedom index is related to domestic inequality from 1970 to 2005 and found that international trade freedom is closely related to inequality. Lim and McNelis (2016) proposed a small open economy model to study whether the opening of trade and finance can lead to a strong Gini coefficient. The simulation results discussed that once imported intermediate products are used in the production process, trade liberalization could lead to an increase in income and an improvement in inequality.

By using observations from 17 transition countries from 1990 to 2006, Franco and Gerussi (2013) reported that openness (trade and FDI) can affect income inequality. They conducted a deeper analysis on the trade variables and found that, in the short-term, imports from a developed country can increase the income gap, while, in the long-term, the result shows a negative relationship. One possible reason can be attributed to the fact that imports from developed countries can bring in new technology and knowledge, which can be disseminated through the imitation processes, and reduce income inequality. Recently, by using data from 23 economic co-operation and development economies from 1990 to 2009, Auguste (2018) revealed that the impact of international trade can increase income inequality, which is in line with Anderson (2005). Conversely, Darku and Yeboah (2018) applied a dynamic standard endogenous growth model to employ a comparative analysis in regions with different economic growth levels such as high-performing Asian countries, Sub-Saharan Africa, and South East Asia; they found that trade openness can reduce income gap for developing regions like Sub-Saharan Africa and South East Asia, as well as high-performing Asian countries.

The rapid development of urbanization in China gained attention of many researchers. In this context, these researchers explored whether urbanization boosts farmers’ incomes? Li (2014) revealed that urbanization increases farmers’ income; the study also highlighted the gap between the north and the south of China, with the farmers in southern China having a stronger income effect than those in north China. However, the relationship related to the urbanization and rural-urban income gap has also been widely analyzed by region-based studies for China (Yang and Xu, 2010; Cao, 2010; Li et al., 2014; Su et al., 2015; Tian et al. 2016; Wu and Rao, 2017). By studying the regional differences between urbanization and income disparity in China through Granger causality, Su et al. (2015) revealed that urbanization can reduce the income gap. The influence is more significant in the eastern China. Wu and Rao (2017) also reported similar results as that of Su et al. (2015). Recently, Wu and Rao (2017), using provincial panel data from 20 provinces in China, reported an inverted-U structure between these two variables. According to the inverted-U relationship, the urbanization threshold level of about 0.5 is determined. On this basis, the level of inequality may be reduced in provinces with a higher urbanization level. They also found that the income inequality between urban and rural areas in provinces with relatively high per capita GDP (i.e. Beijing, Inner Mongolia, Guangdong) is low and the urban-rural wage gap can significantly contribute to income inequality. Some parts of the western region can also find the role of urbanization in narrowing the income gap. Similarly, Pu and Zhu (2018) used panel data from China from 2002 to 2016 and found an inverted-U relationship, which is in line with Wu and Rao (2017). They explained that urbanization increases the urban-rural income gap at first, and subsequently, reduces the inequality (Table 1).

Table 1.

Summary of studies

Internet development and income distribution
Authors	Data sample	Indicators	Observations
Najarzadeh et al. (2014)	Annual data of 108 countries from 1996 to 2010	Number of Internet users, labor productivity, gross capital formation, trade volume	• Internet can positively affect labor productivity. • 1% Internet users ↗⇒ $8.16-14.6 GDP per person ↗.
Whitacre et al.(2014)	County-level data from 2008 to 2011	Broadband adoption, total employment , household income	• Broadband adoption↗⇒ household income↗.
Hübler and Hartje(2016)	Over 5,000 households’ survey data in the rural Southeast Asian area	Technology diffusion, smartphone ownership, off-farm employment, self-employment, households’ income	• Smartphones can positively affect individuals’ income in rural regions for developing countries.
Gao et al.(2018)	Provincial panel data for China from 2002 to 2013	Internet penetration rate, rural residents’ income, urban-rural income gap, digital divide	• Rural computer penetration↗⇒ farmers’ income↗⇒ urban-rural income gap ↘.
Ma et al.(2018)	Survey data from 493 rural households in China	Smartphone use, household income, off-farm work, off-farm income, farm income	• Smartphone use↗⇒ household income ↗. • Households who are smartphone users earned 2643 yuan more per capita annual income, compared to smartphone-free peers.
Celbis and Crombrugghe(2018)	Regional data for Turkey from 1999 to 2011	• Internet infrastructure, GDP, population, land infrastructure index, regional gross value added, air transport capacity	Internet infrastructure can reduce the regional per capita income disparities.
Tchamyou et al. (2019)	County-level data for 48 African countries from 1996 to 2014	Gini index, Atkinson, Palma ratio, mobile phone, internet, fixed broadband, financial access	• ICT reduced inequality through the formal financial sector.
Siaw et al.(2020)	Survey data from 478 rural farmers from two regions in Ghana	Internet use, off-farm , employment, education, access to credit, non-fixed asset (NFA), age, perception variables.	• Internet use can increase farm income and household income by 20.1% and 15.47%.
Mora-Rivera and García-Mora (2021)	Mexico’s 2016 National Household Income and Expenditure Survey (ENIGH)	Housing quality and spaces, access to basic housing services, educational backwardness, access to food, access to social security, access to health services	• Internet access may help reduce poverty levels in Mexico.
Economic factors and income distribution
Authors	Data sample	Indicators	Observations
Bergh and Nilsson(2010)	Panel data covering around 80 countries from 1970 to 2005	GDP per capita, net income Gini coefficient, human capital, dependency ratio, EFI and KOF indices	• In rich countries, economic freedom seems to increase inequality.
Chintrakarnet al.(2012)	Panel data of U.S. states from 1977 to 2001	Inward FDI stocks, income inequality	• Inward FDI can reduce income gap in the long-term. • This negative relationship is heterogeneous: some states in the United States showed a positive association.
Herzer and Nunnenkamp (2013)	Annual data of eight European countries from 1980 to 2000.	Inward FDI, outward FDI, income inequality	• In short-term, inward and outward FDI ↗⇒income gap↗. • In long-term, inward and outward FDI ↗⇔ income gap↘.
Li et al.(2014)	Provincial level data for China from 2004 to 2010	Urbanization, urban-rural income gap, fixed asset investment, average education years, government expenditure	• Urbanization can reduce the income gap. • Additionally, such negative relationship has a lasting impact on all provinces.
Asteriou et al. (2014)	Annual data of EU-27 countries over the period 1995–2009	Gini coefficient, capital account openness, trade openness, secondary education, FDI, ICT, GDP	• The highest contribution to inequality stems from FDI.
Su et al.(2015)	Annual data for 30 provinces in China from 1978 to 2011	Urbanization, urban-rural income gap, population,	• Urbanization can lead to urban-rural income gap. • The influence is more significant in the eastern China.
Ucal et al.(2016)	Annual Turkish data from 1970 to 2008	FDI stock, trade openness, household income inequality	• FDI can lower the level of income inequality in both short- and long-terms.
Auguste(2018)	Data for 23 economic co-operation and development countries from 1990 to 2009	International trade, FDI, immigration, social protection spending, income inequality	• International trade can increase income inequality.
Chang et al.(2018)	Annual data of the United States from 1917 to 2012	U.S. per capita real GDP, income inequality	• GDP per capita can positively affect income inequality. • Causality relationship from GDP per capita to income inequality.

Empirical methodology

Models

The current study evaluates the effects of Internet development on urban-rural income gap in China. In addition, we also consider other economic factors that may influence the urban-rural income gap including FDI, trade openness, urbanization, and regional gross domestic product.

The increase of FDI promotes employment, knowledge spillover and local economic growth, thus helping to increase rural household income levels and narrow the urban-rural income gap (Chen, 2016). A higher level of trade openness promotes labor mobility from rural to urban areas, which helps to reduce rural surplus labor and improve the income level of rural residents (Wei et al., 2013). Urbanization may widen or narrow the urban-rural income gap due to factors such as labor mobility, urban bias policies, and differences in urban and rural human capital (Su et al. 2015). Furthermore, income inequality is closely related to the level of economic development (Huang et al., 2020). In regions with higher levels of economic development, local governments have more resources and capacity to focus on social equity and the income distribution gap, thus promoting the reduction of the urban-rural income gap (Ye et al., 2018, Su et al., 2019).

The fixed effect model and random effect model, a popular and useful equation, explain socioeconomic problems. Therefore, we set up two empirical models that take the following forms.

Model I was set up such that the dependent variable represents the urban-rural income gap (GAP). The independent variables were Internet development (Internet), foreign direct invest (FDI), gross regional domestic product (REDL), trade openness (TO), urbanization (URB).

l n G A P_{i t} = β_{0} + β_{1} l n I n t e r n e t_{i t} + β_{2} l n F D I_{i t} + β_{3} l n R E D L_{i t} + β_{4} T O_{i t} + β_{5} U R B_{i t} + ϵ_{i t}

where β is constant; ∊_t is the stochastic error term; i is the province and t is time.

Model II was set up such that the dependent variable represents the urban-rural income gap (GAP), while the independent variables were Internet*FDI²(IN*FDI), Internet*urbanization³(IN*URB), and Internet*openness⁴(IN*TO). Additionally, the development of Internet has contributed toward the technological requirement of many industries; this factor has also played an important role in enabling China to attract FDI. At the same time, the Internet improves the quality of urbanization and expands the opening to external markets. Therefore, this paper studies how the Internet affects the urban-rural income gap through these factors.

l n G A P_{i t} = β_{0} + β_{1} l n I N * F D I_{i t} + β_{2} l n I N * U R B_{i t} + β_{3} l n I N * T O_{i t} + ϵ_{i t}

where β is constant; ∊_t is the stochastic error term; i is the province and t is time.

Variable selection

Urban-rural income gap (GAP). It is a measure of the income gap between urban and rural residents. This study uses the ratio of disposable income of urban residents in Chinese provinces to the disposable income of rural residents as the income gap between urban and rural residents. GAP is the interpreted variable of this study.

Internet development (Internet). It is the indicator of Internet penetration rate. Internet development is the core explanatory variable of this study. According to Romer (1986), Lucas’ (1988) endogenous economic growth theory, the Internet popularizes ways to improve the productivity of the labor force to improve production efficiency and the income of residents increases. In the early days of Internet development, due to the low information acquisition capacity of rural residents, especially in terms of information screening, utilization, and processing, rural residents were in a relatively inferior position. The income effect of Internet popularization on urban residents was higher than that in rural areas. During this period, the income gap between urban and rural residents gradually widened. During the gradual maturity of the Internet, developed information and communication technologies have greatly improved the ability of rural residents to process information and increase their income. At the same time, the Internet promotes the flow of urban and rural production factors and drives the rapid development of the rural industrial chain. The popularity of the Internet to increase the income of rural residents can reduce the income gap between urban and rural residents. This result is consistent with Scheerder et al. (2017). To narrow the urban-rural income gap through Internet use, both urban and rural residents need access to digital networks. Therefore, we measure Internet development using the overall penetration rate of the Internet in China.

Foreign direct investment (FDI). Affected by the bias of dual economic and policy measures, FDI has mostly flowed into high-end industries after entering China, resulting in a growing gap between rich and poor. At the same time, FDI has pushed rural surplus labor and skilled labor from the agricultural sector to the non-agricultural sector. FDI raises the income level of rural surplus labor and narrows the income gap to a certain extent. The trade effects brought about by the surrounding areas effect the income gap (Wu and Rao, 2017; Auguste, 2018).

Regional economic development level (REDL). The level of economic development is an important factor affecting the income gap between urban and rural areas. Kuznets (1955) mentioned in the inverted U-shaped curve hypothesis that income distribution changes with economic development. When the national per capita income is low to medium, the income distribution tends to deteriorate. With the economic development, the distribution situation has gradually improved and the gap has decreased (Celbis and Crombrugghe, 2018).

Trade openness (TO). The degree of trade openness increases the wage income and asset investment income of urban residents through the market price mechanism and the international differences in production factors. At the same time, trade openness will reduce the compensation of rural residents for agricultural work and savings investment. Since such labor factors cannot flow internationally, the income of rural residents is less than that of urban residents, and the income gap is widening. Here, trade openness is measured by the ratio of total imports and exports to GDP in 31 provinces in China (Asteriou et al., 2014).

Urbanization (URB). Urbanization has led to the transfer of a large amount of rural surplus labor to urban areas. With the gradual saturation of the labor market in urban areas, the wage level in the region has gradually declined, and the wage levels in the two regions have finally tended to converge. At this time, urbanization can narrow the income gap and achieve urban-rural integration. URB is measured by the ratio of the urban population in China’s provinces to the total population at the end of the year (Yang and Xu, 2010; Li et al., 2014; Su et al., 2015).

Data

We employed a 12-year panel dataset of 31 Chinese provinces, comprising 11 eastern regions, 8 central regions, and 12 western regions over the 2005–2016 period. These regions are listed elaborately (see Table 2).

Table 2.

Descriptive three major regions

Factors		Regions
Geography	Eastern Region	Beijing, Fujian, Guangdong, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, Zhejiang
	Central Region	Anhui, Heilongjiang, Henan, Hubei, Hunan, Jiangxi, Jilin, Shanxi
	Western Region	Chongqing, Gansu, Guangxi, Guizhou, Inner Mongolia, Ningxia, Qinghai, Sichuan, Shaanxi, Xinjiang, Xizang, Yunnan

The dependent variable urban-rural income gap is calculated, the relevant income of urban and rural residents were drawn from the statistical yearbooks of 31 provinces. The key components of the independent variable included Internet development. The Internet penetration rate used to represent the Internet development was drawn from the China Internet Network Information Center (CNNIC), which has released a statistical report on the development of Internet in China. Data pertaining to the control variables—such as FDI, regional economic development level and regional economic development level—were drawn from China’s National Bureau of Statistics’ database. Additionally, the urbanization data related to the proportion of urban population data were collected from regional statistical yearbooks. Finally, another key independent variable comprises interactions, which are Internet*FDI, Internet*urbanization, and Internet*trade openness. Table 3 summarizes the descriptive statistics of the variables used in the current study.

Table 3.

Descriptive statistics for variable measures

Variables	Mean	Median	Max	Min	Std. Dev.
lnFDI	12.339	12.762	15.089	7.048	1.794
lnGAP	0.934	0.955	1.524	0.377	0.286
lnREDL	10.313	10.385	11.680	8.527	0.630
lnIN	3.317	3.546	4.354	1.029	0.732
lnTO	5.665	5.314	8.784	3.881	1.049
lnURB	3.900	3.906	4.495	3.037	0.286
lnIN*FDI	15.656	16.140	19.086	8.242	2.218
lnIN*URB	7.217	7.424	8.814	4.231	0.940
lnIN*TO	8.982	8.766	12.630	5.278	1.502

Result

Unit root test

Using provincial panel data to analyze the effects of Internet development—on income gap, the unit root process test is conducted to check whether the data is stable. If there is unit root in the time series, then the process will not be considered stable, which will lead to pseudo regression. The unit root test was conducted on the level to the first difference and all variables were found to be stable. The result of the data stationarity can be shown in the Table 4.

Table 4.

The unit root test results

Categories	Level		1st Difference
Categories	Statistics	Prob. value***	Statistics	Porb. value***
lnGAP
ADF - Fisher Chi-square	10.2637	1	84.8104	0.028
PP - Fisher Chi-square	10.0438	1	180.974	0
lnFDI
ADF - Fisher Chi-square	70.1704	0.2227	120.647	0
PP - Fisher Chi-square	154.515	0	166.941	0
lnTO
ADF - Fisher Chi-square	60.2309	0.54	96.4886	0.0033
PP - Fisher Chi-square	65.4501	0.358	176.112	0
lnURB
ADF - Fisher Chi-square	35.137	0.9976	83.6323	0.035
PP - Fisher Chi-square	124.255	0	168.8	0
lnIN				0
ADF - Fisher Chi-square	198.647	0	120.870	0
PP - Fisher Chi-square	53.8099	0.7612	172.636
lnREDL
ADF - Fisher Chi-square	22.1346	1	131.749	0.000
PP - Fisher Chi-square	19.7518	1	258.575	0
lnIN*FDI
ADF - Fisher Chi-square	25.911	1	135.72	0
PP - Fisher Chi-square	20.95	1	293.32	0
lnIN*TO
ADF - Fisher Chi-square	27.5048	1	135.496	0
PP - Fisher Chi-square	15.0908	1	293.320	0
lnIN*URB
ADF - Fisher Chi-square	184.077	0	117.27	0
PP - Fisher Chi-square	52.3588	0.8036	180.194	0

Results of static panel estimations

To examine the impact of Internet development on the urban-rural income gap in China, we consider static panel estimation techniques—fixed effect and random effect models. We first test Internet development and other economic factors on the urban-rural income gap from the perspective of China as a whole. Second, we divide China into three regions—eastern, central, and western regions—to investigate the transmission mechanism of economic factors affecting the urban-rural income gap through Internet development.

First, from the overall perspective, the fixed effect model is adopted after the Hausman test (see Table 5) and the results show that Internet development has a significant positive effect on the urban-rural income gap (b = 0.279, t = 7.188). It means that a 1% increase in Internet development was found to increase the urban-rural income gap by 0.279%. According to the data of China Statistical Yearbook, the number of Internet broadband access users in China increased by 225 million from 2015 to 2020, including 146.178 million in cities and 77.916 million in rural areas. It shows that the improvement of China’s Internet usage rate depends more on the improvement of urban residents’ usage level, and that the improvement of Internet use level is beneficial to urban residents. Therefore, the uneven development between urban and rural areas, the difference in the ability of urban and rural residents to use the Internet, and the different characteristics of using the Internet to obtain information between urban and rural areas all contribute to the widening of the urban-rural income gap. Trade openness and FDI did not affect the urban-rural income gap (TO: b = 0.032, t = 1.609; FDI: b = 0.016, t = 0.877). However, urbanization has a negative impact on the urban-rural income gap (b = -2.108, t = -9.999). It means that an increase of 1% on urbanization will decrease the urban-rural income gap by 2.108%. This may occur because the acceleration of urbanization can provide more employment opportunities and higher wage levels, and make the surplus agricultural labor force inclined to transfer to urban areas for employment, thus increasing the income of rural residents and narrowing the income gap between urban and rural areas. Additionally, the regional economic development level has a negative impact on the urban-rural income gap (b = -0.421, t = -5.955). It means that an increase of 1% on regional economic development level will decrease urban-rural income gap by 0.421%. This may occur because with the continuous economic growth, more jobs are created in rural areas under the promotion of informatization and local policies, the income level and income structure of farmers have been well optimized, and the income gap between urban and rural sectors will gradually narrow.

When we examined the transmission mechanism, we found that the interaction of Internet development with trade openness was more significant on the urban-rural income gap (b = 0.053, t = 1.962). It means the more the Internet development and trade openness, the more would be the positive impact on the urban-rural income gap in China. In other words, a 1% increase in Internet development with trade openness will increase the urban-rural income gap by 0.053%. This may occur because the Internet development promotes interaction with the outside world, which will increase the number of urban jobs to some extent, thus widening the gap between urban and rural areas. However, the interaction of Internet development with urbanization has a stronger negative effect on the urban-rural income gap (b = -0.289, t = -6.326). It means that a 1% increase in Internet development with urbanization will decrease urban-rural income gap by 0.289%. This result not only supports the above research results, but also shows that the Internet development accelerates urbanization, which further narrows the urban-rural income gap. Additionally, the interaction of Internet development with FDI has no effect on the urban-rural income gap (b = -0.001, t = 0.079).

Table 5.

Overall regression analysis results for China

Categories	All
Categories	Model1	Model2
lnIN	0.279*** [7.188]
lnTO	0.032 [1.609]
lnFDI	0.016 [0.877]
lnURB	-2.108*** [-9.999]
lnREDL	-0.421*** [-5.955]
lnIN*TO		0.053** [1.962]
lnIN*FDI		-0.001 [-0.079]
lnIN*URB		-0.289*** [-6.326]
R-squared	0.772	0.586
F-statistic	32.442	14.480
Hausman Test	165.610 (0.000)	13.070 (0.004)

Note: *denote test statistic significance at the 10% level, **at the 5%, ***at the 1%.

In keeping with the objective of this research, we also estimated the influence of trade openness, Internet development, and other factors on the income gap in each of the eastern, central, and western regions. Models 3, 6, 7, and 8 adopt the fixed effect model, and Models 4 and 5 adopt the random effect model, as explained in the section on methodology. The coefficients are displayed below (see Table 6).

In the eastern region of China, Internet development is estimated to have a significant adverse effect on the urban-rural income gap (b = 0.258, t = 5.1633). The coefficient of Internet development is by 0.258, which means if the Internet development increases by 1%, then the urban-rural income gap will increase by 0.258%. Additionally, trade openness also aggravates the urban-rural income gap (b = 0.060, t = 2.528). It means that an increase of 1% in trade openness will increase the urban-rural income gap by 0.060%. In addition to the fact that FDI has no impact on the urban-rural income gap (b = -0.017, t = -0.542) in eastern China, it must also be noted that urbanization and regional economic development level can fill the urban and rural gap (urbanization: b = -1.992, t = -6.339; GDP: b = -0.318, t = -3.738). It means that an increase of 1% in urbanization and regional economic development level will reduce the urban-rural income gap by 1.992% and 0.318%, respectively.

Moreover, it is worth highlighting that Internet development with trade openness has a positive effect on the urban-rural income gap (b = 0.124, t = 4.786) by the coefficient value 0.124. It implies that a 1% increase in Internet development with trade openness will increase the urban-rural income gap by 0.124%. Additionally, Internet development with FDI did not affect the urban-rural income gap (b = -0.017, t = -0.703). However, Internet development with urbanization has the opposite impact. It has a negative effect on the urban-rural income gap (b = -0.263, t = -5.877). Additionally, it indicates that that a 1% improvement in Internet development with urbanization reduces the urban-rural income gap by 0.263% in eastern region.

In the central region of China, the coefficient of Internet development is positive and significant (b = 0.388, t = 5.233). This implies that a 1% increase in Internet development leads to a 0.388% increase in income gap between urban and rural areas. However, FDI, urbanization, and GDP have negative impacts on the income gap between urban and rural areas. Precisely, a 1% increase in FDI, urbanization, and regional economic development level may reduce the urban-rural income gap by 0.105%, 0.756%, and 0.686%, respectively. Additionally, in terms of transmission mechanisms, only Internet development with FDI is significant, presenting a more significant negative sign in the case of the urban-rural income gap (b = -0.265, t = -3.580). It shows that a 1% increase in Internet development with FDI decreases the urban-rural income gap by 0.265%. In addition, trade openness, Internet development with trade openness, and Internet development with urbanization has no impact on the urban-rural income gap in the central region of China.

In the western region of China, the Internet development is significant, presenting a more significant positive sign on the income gap between urban and rural areas (b = 0.203, t = 2.749). Precisely, a 1% increase in Internet development will increase the urban-rural income gap by 0.203%. Conversely, urbanization plays a negative impact on the urban-rural income gap (b = -2.801, t = -5.245). It means that an increase of 1% on the urbanization will decrease the urban-rural income gap by 2.801%. When Internet development with urbanization increases, the urban-rural income gap will also be narrowed (b = -0.318, t = -4.494). The coefficient of Internet development with urbanization is by 0.318; it means that if Internet development with urbanization increase by 1%, the urban-rural income gap will reduce by 0.318%. Furthermore, the other factors such as trade openness, FDI, GDP, Internet development with trade openness, and Internet development with FDI have no effect on the income gap between urban and rural areas.

According to the above empirical results of eastern, central, and western regions in China, it can be seen that the impact of Internet development on the urban-rural income gap in central China is far greater than that in eastern and western China. In other words, the Internet development will make the urban-rural income gap in central regions wider than that in the eastern and western regions. Additionally, trade openness expands the urban-rural income gap in the eastern region. FDI reduces the urban-rural income gap, but only in the central regions. Urbanization reduces the urban-rural income gap in the western region more than that in the eastern and central regions. Additionally, regional economic development level bridges the urban-rural income gap in central regions more than that in the eastern regions, but it has no effect in western regions.

Table 6.

Comparative analysis result in the eastern, central, and western regions

Categories	Geography
	Eastern		Central		Western
	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
lnIN	0.258*** [5.1633]		0.388*** [5.233]		0.203*** [2.749]
lnTO	0.060** [2.528]		-0.003 [-0.064]		0.001 [0.041]
lnFDI	-0.017 [-0.542]		-0.105*** [-3.911]		0.040 [1.454]
lnURB	-1.992*** [-6.339]		-0.756*** [-3.573]		-2.801*** [-5.245]
lnREDL	-0.318*** [-3.738]		-0.686*** [-5.771]		-0.206 [-1.095]
lnIN*TO		0.124*** [4.786]		0.049 [0.641]		0.002 [0.049]
lnIN*FDI		-0.017 [-0.703]		-0.265*** [-3.580]		0.029 [0.788]
lnIN*URB		-0.263*** [-5.877]		0.160 [1.123]		-0.318*** [-4.494]
R-squared	0.736	0.419	0.739	0.633	0.751	0.539
F-statistic	21.570	30.883	51.191	14.707	23.877	10.709
Hausman Test	56.355 (0.0000)	1.568 (0.666)	7.982 (0.157)	7.965 (0.046)	69.042 (0.0000)	7.285 (0.063)

Note: *denote test statistic significance at the 10% level, **at the 5%, ***at the 1%.

Conclusion

The Internet, as the core productivity of the information age, has gradually become the main way of information dissemination in modern society. It has played a significant role in promoting China’s economic development and is conducive to alleviating the imbalance between urban and rural development in China’s current economic development. However, there are differences in the development of urban and rural areas. The uneven distribution of resources between urban and rural areas also increases the imbalance of regional development to a certain extent and enlarges the discrepancy between urban and rural areas. Moreover, the urban-rural income gap has always been a major problem in China’s economic and social development. There is certain theoretical and practical significance in analyzing the urban-rural income gap from the perspective of Internet development. This study investigated the effects of the Internet development on urban-rural income gap from the perspective of China as a whole, and employed empirical models to analyze and compare the effects of the eastern, central, and western regions using a provincial level panel dataset comprising 31 provinces in China during 2005–2016.

Our findings may be summarized in the following words. The empirical results confirmed that Internet development aggravates the urban-rural income gap in China. Specifically, it affects the central region much more than the eastern and western regions. The trade openness expands the urban-rural income gap in the eastern region. However, urbanization minimizes the urban-rural income gap. A comparative analysis of the eastern, central, and western regions reveals that urbanization reduces the urban-rural income gap in the western region more than that in the eastern and central regions. The regional economic development level also reduces the urban-rural income gap, especially in central region more than that in the eastern region. However, it has no effect on the urban-rural income gap in the western region. Additionally, FDI reduces urban-rural income gap only in the central region.

Additionally, Internet development with trade openness worsens the urban-rural income gap in China, especially in eastern regions. However, urban-rural income gap in the central region can decrease by the contributions of both Internet development with FDI. Meanwhile, Internet development with urbanization can play a key role in decreasing urban-rural income gap in western region more than that in eastern region.

The main policy implications can be showed from our study. First, to further increase the openness of the Internet, strengthening the Internet network construction in rural areas is needed. This will be conducive to the labor market resource allocation function, make full use of network information retrieval, and take advantage of information spread with high efficiency and low cost implementation — thus realizing entrepreneurship, employment in rural areas and the balanced development of a regional economy. This is key to solving the problem of the income gap between urban and rural areas. Second, eastern region with the highest level of economic development and the earliest implementation of the coastal opening-up policies, should give full play to the advantages of the coastal regions, improve trade contacts with other regions, and strengthen the implementation of the policies. Third, FDI in central regions can significantly promote the development of the local rural economy and increase farmers’ income, thus contributing to the narrowing of the urban-rural income gap. Fourth, in China, urbanization is an important way to achieve balanced development between urban and rural areas. In view of the imbalance between urban and rural development and the inadequate development of rural areas, the quality and coordination of regional economic development need more attention. On the one hand, the agricultural transformation and upgrading of rural areas is an opportunity to enhance the internal development of rural power. On the other hand, a new type of urban-rural relationship featuring “promoting rural areas through cities” needs to be built in order to accelerate integrated urban-rural development. Finally, encouraging the eastern region to take the lead in developing and promoting the central region will contribute toward narrowing the urban-rural income gap between the eastern and central regions. Finally, the role of the Internet must be promoted for enhancing trade openness, attracting FDI, enhancing urbanization, strengthening the establishment of the rural information infrastructure, and promoting social harmony.

Footnotes

Notes

Acknowledgements

This work was supported by post-funding of the National Social Science Fund of China, “Research on theoretical framework and empirical issues of high-quality development in China”, [Grant Number: 20FJLB020]; General Project of Philosophy and Social Science Research in Universities of Jiangsu Province [Grant Number: 2020SJA1027].

ORCID iDs

Meiling Wang

Zihui Yin

References

Anderson

(2005) Openness and inequality in developing countries: A review of theory and recent evidence. World Development 33(7): 1045–1063.

Ashmore

Farrington

Skerratt

(2015) Superfast broadband and rural community resilience: Examining the rural need for speed. Scottish Geographical Journal 131(3-4): 265–278.

Asteriou

Dimelis

Moudatsou

(2014) Globalization and income inequality: A panel data econometric approach for the EU27 countries. Economic Modelling 36: 592–599.

Auguste

(2018) Income inequality, globalization, and the welfare state: Evidence from 23 industrial countries, 1990–2009. Sociological Forum 33(3): 666–689.

Bauer

(2018) The internet and income inequality: Socio-economic challenges in a hyper connected society. Telecommunications Policy 42: 333–343.

Bergh

Nilsson

(2010) Do liberalization and globalization increase income inequality?. European Journal of Political Economy 26(4): 488–505.

Cao

(2010) Urban–rural income disparity and urbanization: What is the role of spatial distribution of ethnic groups? A case study of Xinjiang Uyghur Autonomous Region in western China. Regional Studies 44(8): 965–982.

Celbis

de Crombrugghe

(2018) Internet infrastructure and regional convergence: Evidence from Turkey. Papers in Regional Science 97(2): 387–409.

Chang

Rangan Gupta

Stephen

, et al. (2018) Causality between per capita real GDP and income inequality in the U.S.: Evidence from a wavelet analysis. Social Indicators Research 135: 269–289.

10.

Chen

(2016) The impact of foreign direct investment on urban-rural income inequality: Evidence from China. China Agricultural Economic Review 8(3): 480–497.

11.

Chintrakarn

Herzer

Nunnenkamp

(2012) FDI and income inequality: Evidence from a panel of U.S. States. Economic Inquiry 50(3): 788–801.

12.

Darku

Yeboah

(2018) Economic openness and income growth in developing countries: A regional comparative analysis. Applied Economics 50(8): 855–869.

13.

Empirical evidence from transition countries. Journal of International Trade & Economic Development 22(8): 1131–1160.

14.

Franco

Gerussi

(2013) Trade, foreign direct investments (FDI) and income inequality:Empirical evidence from transition countries. Journal of International Trade & Economic Development 22(8): 1131–1160.

15.

Gao

Zang

Sun

(2018) Does computer penetration increase farmers’ income? An empirical study from China. Telecommunications Policy 42: 345–360.

16.

Herzer

Nunnenkamp

(2013) Inward and outward FDI and income inequality: Evidence from Europe. Review of World Economics 149: 395–422.

17.

Herzer

Hühne

Nunnenkamp

(2014) FDI and income inequality: Evidence from Latin American economies. Review of Development Economics 18(4): 778–793.

18.

Huang

Yan

(2016) Assessing urban sustainability of Chinese megacities: 35 years after the economic reform and open-door policy. Landscape and Urban Planning 145: 57–70.

19.

Huang

Luo

(2020) Has highway construction narrowed the urban– rural income gap? Evidence from Chinese cities. Papers in Regional Science 99: 705–723.

20.

Hübler

Hartje

(2016) Are smartphones smart for economic development? Economics Letters 141: 130–133.

21.

Jaumotte

Lall

Papageorgiou

(2013) Rising income inequality: Technology, or trade and financial globalization? IMF Economic Review 61(2): 271–309.

22.

Kuznets

(1955) Economic growth and income inequality. American Economic Review 45(1): 1–28.

23.

Lai

(2011) Foreign direct investment and wage inequality: Evidence from China. World Development 39(8): 1322–1332.

24.

LaRose

Gregg

Strover

, et al. (2007) Closing the rural broadband gap: Promoting adoption of the Internet in rural America. Telecommunications Policy 31(6-7): 359–373.

25.

Zeng

Guo

(2020) E-commerce development and urban-rural income gap: Evidence from Zhejiang Province, China. Papers in Regional Science 100: 475–494.

26.

Wang

Zhu

, et al. (2014) Assessing the spatial and temporal differences in the impacts of factor allocation and urbanization on urban-rural income disparity in China, 2004-2010. Habitat International 42: 76–82.

27.

Lim

McNelis

(2016) Income growth and inequality: The threshold effects of trade and financial openness. Economic Modelling 58: 403–412.

28.

Lin

Kim

Lee

(2015) Financial development and the FDI-inequality nexus. Contemporary Economic Policy 33(3): 513–534.

29.

Lucas

Jr RE

(1988) On the mechanics of economic development. Journal of Monetary Economics 22(1): 3–42.

30.

Grafton

Renwick

(2018) Smartphone use and income growth in rural China: empirical results and policy implications. Electronic Commerce Research 1–24.

31.

Renwick

Nie

, et al. (2018) Off-farm work, smartphone use and household income: Evidence from rural China. China Economic Review 52: 80–94.

32.

Mack

Faggian

(2013) Productivity and broadband: The human factor. International Regional Science Review 36(3): 392–423.

33.

Močnik

Širec

(2010) The determinants of internet use controlling for income level: Cross-country empirical evidence. Information Economics and Policy 22: 243–256.

34.

Mora-Rivera

García-Mora

(2021) Internet access and poverty reduction: Evidence from rural and urban Mexico. Telecommunications Policy 45(2): 102076.

35.

Najarzadeh

Rahimzadeh

Reed

(2014) Does the Internet increase labor productivity? Evidence from a cross-country dynamic panel. Journal of Policy Modeling 36(6): 986–993.

36.

Noh

Yoo

(2008) Internet, inequality and growth. Journal of Policy Modeling 30(6): 1005–1016.

37.

Paramati

Nguyen

TPT

(2018) Does financial market growth improve income distribution? A comparison of developed and emerging market economies of the global sample. International Journal of Finance & Economics 24(1): 1–18.

38.

Zhu

(2018) Research on the influence of urbanization on income gap between urban and rural areas–Based on income source. Open Journal of Social Sciences 6: 132–141.

39.

Romer

(1986) Increasing returns and long-run growth. Journal of Political Economy 94(5): 1002–1037.

40.

Qiu

Zhong

Sun

, et al. (2021) Is internet penetration narrowing the rural–urban income inequality? A cross-regional study of China. Quality & Quantity 4: 1–20.

41.

Scheerder

van Deursen

van Dijk

(2017) Determinants of Internet skills, uses and outcomes. A systematic review of the second-and third-level digital divide. Telematics and Informatics 34(8): 1607–1624.

42.

Siaw

Jiang

Twumasi

, et al. (2020) The Impact of Internet Use on Income: The Case of Rural Ghana. Sustainability 12(8): 3255.

43.

Liu

Chang

, et al. (2015) Is urbanization narrowing the urban-rural income gap? A cross regional study of China. Habitat International 48: 79–86.

44.

Tao

(2019). Is financial development narrowing the urban– rural income gap? A cross-regional study of China. Papers in Regional Science 98:1779–1800.

45.

Tchamyou

Erreygers

Cassimon

(2019) Inequality, ICT and financial access in Africa. Technological Forecasting and Social Change 139: 169–184.

46.

Tian

Guo

Zheng

(2016) Urbanization and rural livelihoods: A case study from Jiangxi Province, China. Journal of Rural Studies 47: 577–587.

47.

Tian

Zhang

Zhou

, et al. (2016) Regional income inequality in China revisited: A perspective from club convergence. Economic Modelling 56: 50–58.

48.

Ucal

Haug

Bilgin

(2016) Income inequality and FDI: Evidence with Turkish data. Applied Economics 48(11): 1030–1045.

49.

Wei

Guo

(2013) Trade structure, trade mode and the urban-rural income gap in China. South African Journal of Economic and Management Sciences, 16(2): 96–114.

50.

Whitacre

Gallardo

Strover

(2014) Does rural broadband impact jobs and income? Evidence from spatial and first-differenced regressions. Annals of Regional Science 53: 649–670.

51.

Whitacre

(2010) The diffusion of internet technologies to rural communities: A portrait of broadband supply and demand. American Behavioral Scientist 53(9): 1283–1303.

52.

Whitacre

Mills

(2007) Infrastructure and the rural—urban divide in high-speed residential Internet access. International Regional Science Review 30(3): 249–273.

53.

Whitacre

Mills

(2010) A need for speed? Rural Internet connectivity and the no access/dial-up/high-speed decision. Applied Economics 42(15): 1889–1905.

54.

Whitacre

Alam

Lobo

(2018) Econometric error nullifies finding of the impact of broadband speed on county-level employment. Information Economics and Policy 44: 58–60.

55.

Rao

(2017) Urbanization and income inequality in China: An empirical investigation at provincial level. Social Indicators Research 131(1): 189–214.

56.

Yang

(2010) The relationship between the urban–rural income inequality and urbanization in West China: A case study of Qinghai Province. Progress in Geography 29(8): 961–967.

57.

Qian

(2018) Financial development, urbanization, and urban-rural income disparity: Evidence based on Chinese provincial data. Modern Economy 9: 31–60.

58.

Zhang

(2013) Income disparity and digital divide: The Internet Consumption Model and cross-country empirical research. Telecommunications Policy 37: 515–529.